Generate sigmoidal IC50 curves with confidence intervals and Hill equation fitting used in drug discovery and toxicology - from single compounds to multi-compound batch comparisons.
Dose-response relationships underpin drug discovery, toxicology, and receptor pharmacology. The 4-parameter logistic model captures the full sigmoidal curve and extracts IC50, EC50, Hill coefficient, and Emax - quantities that appear in every compound report.
4PL Hill equation fit with IC50 annotation on log scale - the standard for drug potency reporting
Overlaid curves for 3-6 compounds with IC50 table for direct potency comparison
Bootstrap or parametric 95% CI around the fitted curve to show estimation uncertainty
Compare full and inhibited Hill curves to calculate Emax ratio and quantify antagonism
Plot individual replicates behind the mean curve for transparent data reporting
Normalized 0-100% curves comparing partial and full agonists at matched receptor systems
scipy.optimize.curve_fit fits the 4PL model and extracts IC50 with error bound automatically - no manual parameter tuning.
Detects concentration data and auto-switches to log x-axis with correct tick formatting for concentration spanning multiple orders of magnitude.
Upload a wide-format CSV and get one curve per compound column automatically with a consistent color palette and IC50 legend.
Single-command 300 DPI export at exact journal column width with Arial font - no post-processing in Illustrator needed.
Fits a 4-parameter logistic Hill equation to simulated replicate data. IC50 is extracted from the fit and annotated directly on the plot with a dashed marker line.
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Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Try with your data
Start with one of our curated pharmacology datasets or upload your own concentration-response CSV.
Cell viability vs concentration for multiple drug compounds
Substrate concentration vs reaction rate for Michaelis-Menten fitting
UV-Vis absorbance data for protein quantification standard curve
OD600 growth data over time for logistic fit and doubling time
Four compounds are fit simultaneously using the same 4PL model. IC50 values appear in the legend and are marked on each curve at the 50% viability intercept - the standard visual presentation in drug discovery screening reports.
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Running once automatically on first load
This is a safe playground for learning! Try changing:
Edit the code, run it, then open the full data visualization tool to continue with your own dataset.
Chart gallery
Interactive examples with ready-to-run code
Combines box plots with kernel density to show distribution shape across groups.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.stats import f_oneway
# Generate exam score data for 3 groups
np.random.seed(42)
control = np.random.normal(72, 12, 50)
treatment_a = np.random.normal(78, 10, 50)
Represents data values as colors in a two-dimensional matrix format.
Sample code / prompt
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
[1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
[0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],Upload your concentration-response CSV, describe the compounds you want to compare, and get publication-ready dose-response figures with full Python code in seconds.
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